This invention relates to measurements of the surface of a person's body. The term “measurement” as used herein refers to the establishment of a distance, angle, volume, or qualitative description pertaining to one or more portions of an individual's body. Examples of measurements include: the circumference of a person's waist, hip, neck, head, torso, or upper arm; the length of an arm from shoulder to wrist, or of a leg from hip to ankle; the length or width of a foot; an individual's height; the volume and outline of space occupied by an individual's abdomen; or the curve in space that would be described by a ribbon placed starting at the small of the back, then following the surface of the skin across the crotch and up to the navel.
An important use of measurements is to size individuals for apparel, accessories, or prosthetics. The term “apparel” may encompass garments of any kind: shirts, trousers, shoes, hats, etc. The term “accessories” may encompass items that are carried but not generally considered clothing: jewelry, purses, etc. The term “prosthetics” may encompasses devices that replace or augment body parts that are missing or damaged; artificial limbs, orthotic shoes, etc.
It is clear that body measurements may be used for many other purposes—such as surgical planning, fitness applications, and biometrics—and although this document will focus on sizing individuals for garments, the scope of the system and method includes all applications that may be realized through measurements of a person's body.
Manual measurement of a person's body has been conducted for thousands of years. Today, the most common way of measuring a person—for example, for sizing a suit—is with a fabric tape measure (often abbreviated to “tape”), optionally accompanied by a set of pins, markers, and pre-sized measurement garments. The tape is used to gauge the length, width, or circumference of various body portions—for example, the circumference of the neck, or the length of the arm from shoulder to wrist—and the pins, markers, and pre-sized garments are used to mark off the measurements from the fabric tape measure, and so establish the overall shape or volume of the body portion. For example, a pre-sized shirt may be donned by an individual, and then portions of that shirt marked or pinned to conform to the individual's body shape, in this way creating a physical “shell” or envelope of the person's approximate shape.
Manual measurement suffers from many drawbacks:
It is imprecise. The accuracy of the measurements varies depending on the skill of the measurer, the precise location on the body where the measurements are obtained, the stance of the measured individual, and myriad other factors.
It is perishable. The size of an individual changes over time, due to exercise, growth, diet, or even salt intake (affecting tissue volume), so that measurements become stale or obsolete over time.
It is time-consuming. Measurement for a suit, for example, easily encompasses tens of measurements, each of which may take seconds to minutes to accomplish.
It is inconvenient. Because it is infeasible to measure oneself, it is usually necessary for an individual to visit a tailor or garment store to be measured (or at least have a friend do it). The individual typically must travel to the measurement site, wait to be seen, and finally wait to be measured. There is also the inconvenience or potentially being pricked by sharp pins if they are used during the sizing process.
It is resistant to correction. If errors are made during the measurement process, resulting, say, an ill-fitting shirt, then the individual must return to the measurer to be re-measured. Furthermore, all the measurements may have to be redone from scratch, because it may not be clear which particular measurement was the cause of the mis-sized garment
It is not private. It may be embarrassing or uncomfortable for a person to submit to being measured by someone else.
Accordingly, many schemes have been proposed to partially or fully automate the process of body measurement. Some methods of automation in the prior art involve the use of a 3D body scanner. Such a scanner can obtain a complete, essentially “360-degree” 3D scan of a person's body. Common to such methods is emitting electromagnetic or sound energy—such as laser light or sonar—onto a person's body (wherein such energy is transmitted from multiple different directions or angles), and then mathematically reassembling the reflected electromagnetic or sound pattern (as received, again, from multiple directions or angles) to build a complete computer 3D model. Because of the need to transmit and receive the electromagnetic energy from many different directions, 3D scanners must use either many different transmitters and sensors set up simultaneously at different locations around the consumer; or else use an array of transmitters and sensors that rotate, in a tomographic fashion, around the consumer, typically at a fixed rate of angular change.
This burden of transmitters and sensors means that 3D body scanners are necessarily large, complex devices. For example, Unique Solutions manufactures (as of September 2011) the “MyBestFit” a.k.a. “Intellifit Virtual Fitting Room Bodyscanner”, which is a human-sized stand-alone box-like device within which an individual stands in order to be scanned by “radio waves”. A similar large device is the “NX-16 Whole Body Scanner” manufactured by Shape Analysis Corporation.
3D body scanners suffer from many drawbacks. First, their size and complexity prevent them from being used at home; a consumer must travel to a retail store, or tailor, who happens to have installed a 3D body scanner.
Second, it is difficult to use a 3D scanner to generate the types of sizing that are common in the garment industry; even if a mass of, say, 3D mesh data Is obtained from the scanner, there remains the need to extract measurements of interest, such as arm circumference or foot length, from that same data; and since the measurements could have as easily been obtained from direct measurements of the person in the scanner, this problem begs the question whether the 3D scanner was even needed in the first place.
Third, because home measurements with a 3D scanner are infeasible, 3D body scanners fail to eliminate the problems of inconvenience and perishable measurements.
Fourth, because of the large amount of data gathered, coupled with the need to rotate sensors around the individual or to perforin a raster scan, 3D scanners are typically slow.
Fifth, because 3D scanners cannot be mass-manufactured for use in consumer's homes (for the reasons above), there may be long lines to use the few machines that are assembled, and economies of scale are not available to bring manufacturing costs down.
Sixth, 3D scanners prevent real-time interaction—they do not give real-time feedback, cannot respond in real-time to user commands, and cannot adjust to real-time changes in the user's position. This limits the utility and entertainment value of user interfaces to 3D scanners.
Other methods of automation in the prior art involve the use of special tapes or markers. Common to all such methods is having a person display special markings on their body, such as ruled lines, which can then either be manually entered by an operator or automatically measured by a computer program. These methods of automation are necessarily inconvenient (because either the individual to be measured, or another operator, must take the time to learn, assemble, and place the special markers) and prone to error (since the accuracy of measurement depends on the skill with which the special markers are placed). Indeed, these methods of automation do not really automate the measurement of body portions, so much as “parallelize” the manual process of tape measurements.
Other methods of automation in the prior art utilize computerized images of a person, upon which human operators manually superimpose markings which allow relevant garment measurements to be made. Common to all such methods is the requirement for a second person (the operator) to act upon the digitized data, e.g., by using a computer console to manually highlight landmarks on the computerized images. Therefore, these methods do not automate the act of manual measurement, so much as postpone it to a later time or to a different physical location.
Overall, known methods of automated garment sizing suffer from one or more of the following disadvantages:
Known methods may require the use of bulky and/or expensive devices (e.g., 3D body scanners) that are not suited to home use, but instead require the measured individual to travel to a dedicated location, such as a retail store (meanwhile Taking up valuable real estate in that same retail store);
Known methods may generate a complex mass of 3D data which, while comprehensive, nonetheless fails to address the fundamental need to gather a set of specific measurements (e.g., shoulder-to-wrist length) that are the most useful and relevant to garment sizing;
Similarly, known methods may provide an overwhelming amount of detail that is too impractical to act upon (for example, high-resolution 3D meshes of the entire body), rather than a well-considered smaller set of specific measurements (for example, shoulder-to-wrist length) that is most practical for manufacturing garments;
Known methods may require an individual to assume odd or uncomfortable positions or poses for extended periods of time;
Known methods may require an individual to keep moving in particular ways, such as rotating continually in circles or adopting a variety of poses, which may distract the individual: prevent the individual from viewing real-time feedback or providing real-time operation of the measurement device; or be difficult for the individual to perform (e.g., if the consumer is physically disabled):
Known methods may be of insufficient precision or resolution to be able to carry out measurements to the desired accuracy;
Known methods may lack the capability to carry out the entire range of measurements needed for accurate custom clothing measurements: for example, circumference of a portion of the body;
Known methods may be subject to confounders that limit their accuracy—for example, a purely visual method may be unable to distinguish accurately between an individual's arm and the same individual's section of torso which the arm overlaps;
Known methods may require substantial manual intervention, by the measured individual and/or by a second person, to fill in “missing measurements”; or to confirm the accuracy of automated measurements; or to operate equipment (such as a 3D scanner); or to place markers/special clothing on the measured individual's body;
Known methods may be too expensive for individual consumers to purchase;
Known methods require the user to remain still for extended periods of time, typically seconds or even minutes;
Known methods take significant time to produce just a single measurement, and if the measurement is erroneous, the measurements must be repeated.
Known methods may not allow real-time, interactive user interfaces, or system responses to user movement or commands.
Known methods may obtain limited depth knowledge about a scene. “Depth knowledge” or “depth data”, as used herein, refers to gathering information—possibly partial, or incomplete—about the spatial positions of objects in space relative to a known coordinate system. “Image knowledge” or “image data”, as used herein, refers to gathering an image of a scene, which may be in visual wavelengths or in other wavelengths of the electromagnetic spectrum, “Color image knowledge” or “color image”, as used herein, refers lo gathering a visual image of a scene, using color wavelengths, similar to the way in which a standard digital camera gathers a visual image. The term “camera”, as used herein, refers to any sensor that may gather information about the environment, especially (though not limited to) electromagnetic measurements, such as visible or infrared light. “Camera”, as used herein, is thus a general-purpose term, and does not refer specifically to, nor is limited to, visual-light devices.
US patent publication 2011-0211044 (Shpunt) teaches a method of gathering depth knowledge about an object through the use of an illumination module, which projects patterned optical radiation onto a scene, and an image capture module, which captures an image of the reflected pattern.
Image and/or depth data may be combined to identify the spatial location of specific human body portions. US patent publication 2011-0052006 (Gurman) teaches a method of locating portions of a humanoid form using a temporal sequence of depth maps, where each depth map represents a scene as a two-dimensional matrix of pixels indicating topographic information. US patent publication 2011-0211754 (Litvak) teaches a method which processes image and depth data in such a way that specific parts of a body, such as the head, may be identified in the image and depth data. Thus, post-processing of image and/or depth data can generate so-called “skeleton data” or “joint data”, describing the approximate locations in space of specific parts of a person's body.